Model fitting by least squares

Next: Null space and iterative Up: KRYLOV SUBSPACE ITERATIVE METHODS Previous: Method of random directions

## Why steepest descent is so slow

Before we can understand why the conjugate-direction method is so fast, we need to see why the steepest-descent method is so slow. The process of selecting is called line search,'' but for a linear problem like the one we have chosen here, we hardly recognize choosing as searching a line. A more graphic understanding of the whole process is possible from considering a two-dimensional space, where the vector of unknowns has just two components, and . Then, the size of the residual vector can be displayed with a contour plot in the plane of . Figure 7 shows a contour plot of the penalty function of . The gradient is perpendicular to the contours. Contours and gradients are curved lines. When we use the steepest-descent method, we start at a point and compute the gradient direction at that point. Then, we begin a straight-line descent in that direction. The gradient direction curves away from our direction of travel, but we continue on our straight line until we have stopped descending and are about to ascend. There we stop, compute another gradient vector, turn in that direction, and descend along a new straight line. The process repeats until we get to the bottom or until we get tired.

yunyue
Figure 7.
Route of steepest descent (black) and route of conjugate direction (light grey or red).

What could be wrong with such a direct strategy? The difficulty is at the stopping locations. These locations occur where the descent direction becomes parallel to the contour lines. (There the path becomes level.) So, after each stop, we turn from parallel to perpendicular to the local contour line for the next descent. What if the final goal is at a angle to our path? A turn cannot be made. Instead of moving like a rain drop down the centerline of a rain gutter, we move along a fine-toothed zigzag path, crossing and recrossing the centerline. The gentler the slope of the rain gutter, the finer the teeth on the zigzag path.

 Model fitting by least squares

Next: Null space and iterative Up: KRYLOV SUBSPACE ITERATIVE METHODS Previous: Method of random directions

2014-12-01